



import inspect
import unittest

from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch
   from torch import nn

   from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
   from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import DPTImageProcessor



class      _UpperCAmelCase      :




     def __init__(					self			,    a__			,    a__=2			,    a__=3_2			,    a__=1_6			,    a__=3			,    a__=True			,    a__=True			,    a__=3_2			,    a__=4			,    a__=[0, 1, 2, 3]			,    a__=4			,    a__=3_7			,    a__="gelu"			,    a__=0.1			,    a__=0.1			,    a__=0.0_2			,    a__=3			,    a__=[1, 3_8_4, 2_4, 2_4]			,    a__=True			,    a__=None			,    ):
         A__					=       parent
         A__					=       batch_size
         A__					=       image_size
         A__					=       patch_size
         A__					=       num_channels
         A__					=       is_training
         A__					=       use_labels
         A__					=       hidden_size
         A__					=       num_hidden_layers
         A__					=       backbone_out_indices
         A__					=       num_attention_heads
         A__					=       intermediate_size
         A__					=       hidden_act
         A__					=       hidden_dropout_prob
         A__					=       attention_probs_dropout_prob
         A__					=       initializer_range
         A__					=       num_labels
         A__					=       backbone_featmap_shape
         A__					=       scope
         A__					=       is_hybrid
         # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
         A__					=       (image_size // patch_size) ** 2
         A__					=       num_patches + 1




     def 		snake_case_		(					self):
         A__					=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

         A__					=       None
         if self.use_labels:
             A__					=       ids_tensor([self.batch_size, self.image_size, self.image_size]			,    self.num_labels)

         A__					=       self.get_config()

         return config, pixel_values, labels




     def 		snake_case_		(					self):
         A__					=       {
             '''global_padding''': '''same''',
             '''layer_type''': '''bottleneck''',
             '''depths''': [3, 4, 9],
             '''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
             '''embedding_dynamic_padding''': True,
             '''hidden_sizes''': [9_6, 1_9_2, 3_8_4, 7_6_8],
             '''num_groups''': 2,
         }

         return DPTConfig(
             image_size=self.image_size			,    patch_size=self.patch_size			,    num_channels=self.num_channels			,    hidden_size=self.hidden_size			,    num_hidden_layers=self.num_hidden_layers			,    backbone_out_indices=self.backbone_out_indices			,    num_attention_heads=self.num_attention_heads			,    intermediate_size=self.intermediate_size			,    hidden_act=self.hidden_act			,    hidden_dropout_prob=self.hidden_dropout_prob			,    attention_probs_dropout_prob=self.attention_probs_dropout_prob			,    is_decoder=__UpperCAmelCase			,    initializer_range=self.initializer_range			,    is_hybrid=self.is_hybrid			,    backbone_config=__UpperCAmelCase			,    backbone_featmap_shape=self.backbone_featmap_shape			,    )




     def 		snake_case_		(					self			,    a__			,    a__			,    a__):
         A__					=       DPTModel(config=__UpperCAmelCase)
         model.to(__UpperCAmelCase)
         model.eval()
         A__					=       model(__UpperCAmelCase)
         self.parent.assertEqual(result.last_hidden_state.shape			,    (self.batch_size, self.seq_length, self.hidden_size))




     def 		snake_case_		(					self			,    a__			,    a__			,    a__):
         A__					=       self.num_labels
         A__					=       DPTForDepthEstimation(__UpperCAmelCase)
         model.to(__UpperCAmelCase)
         model.eval()
         A__					=       model(__UpperCAmelCase)
         self.parent.assertEqual(result.predicted_depth.shape			,    (self.batch_size, self.image_size, self.image_size))




     def 		snake_case_		(					self			,    a__			,    a__			,    a__):
         A__					=       self.num_labels
         A__					=       DPTForSemanticSegmentation(__UpperCAmelCase)
         model.to(__UpperCAmelCase)
         model.eval()
         A__					=       model(__UpperCAmelCase			,    labels=__UpperCAmelCase)
         self.parent.assertEqual(
             result.logits.shape			,    (self.batch_size, self.num_labels, self.image_size, self.image_size))




     def 		snake_case_		(					self):
         A__					=       self.prepare_config_and_inputs()
         A__					=       config_and_inputs
         A__					=       {'''pixel_values''': pixel_values}
         return config, inputs_dict







@require_torch
class      _UpperCAmelCase      (							_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   unittest.TestCase  ):
     UpperCamelCase__		     =					(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
     UpperCamelCase__		     =					(
         {
             '''depth-estimation''': DPTForDepthEstimation,
             '''feature-extraction''': DPTModel,
             '''image-segmentation''': DPTForSemanticSegmentation,
         }
         if is_torch_available()
         else {}
     )

     UpperCamelCase__		     =					False
     UpperCamelCase__		     =					False
     UpperCamelCase__		     =					False




     def 		snake_case_		(					self):
         A__					=       DPTModelTester(self)
         A__					=       ConfigTester(self			,    config_class=__UpperCAmelCase			,    has_text_modality=__UpperCAmelCase			,    hidden_size=3_7)




     def 		snake_case_		(					self):
         self.config_tester.run_common_tests()




     @unittest.skip(reason='''DPT does not use inputs_embeds''')
     def 		snake_case_		(					self):
         pass




     def 		snake_case_		(					self):
         A__					=       self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
             A__					=       model_class(__UpperCAmelCase)
             self.assertIsInstance(model.get_input_embeddings()			,    (nn.Module))
             A__					=       model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(__UpperCAmelCase			,    nn.Linear))




     def 		snake_case_		(					self):
         A__					=       self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
             A__					=       model_class(__UpperCAmelCase)
             A__					=       inspect.signature(model.forward)
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             A__					=       [*signature.parameters.keys()]

             A__					=       ['''pixel_values''']
             self.assertListEqual(arg_names[:1]			,    __UpperCAmelCase)




     def 		snake_case_		(					self):
         A__					=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__UpperCAmelCase)




     def 		snake_case_		(					self):
         A__					=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_depth_estimation(*__UpperCAmelCase)




     def 		snake_case_		(					self):
         A__					=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_semantic_segmentation(*__UpperCAmelCase)




     def 		snake_case_		(					self):
         for model_class in self.all_model_classes:
             if model_class.__name__ == "DPTForDepthEstimation":
                 continue

             A__					=       self.model_tester.prepare_config_and_inputs_for_common()
             A__					=       True

             if model_class in get_values(__UpperCAmelCase):
                 continue

             A__					=       model_class(__UpperCAmelCase)
             model.to(__UpperCAmelCase)
             model.train()
             A__					=       self._prepare_for_class(__UpperCAmelCase			,    __UpperCAmelCase			,    return_labels=__UpperCAmelCase)
             A__					=       model(**__UpperCAmelCase).loss
             loss.backward()




     def 		snake_case_		(					self):
         for model_class in self.all_model_classes:
             if model_class.__name__ == "DPTForDepthEstimation":
                 continue

             A__					=       self.model_tester.prepare_config_and_inputs_for_common()
             A__					=       False
             A__					=       True

             if model_class in get_values(__UpperCAmelCase) or not model_class.supports_gradient_checkpointing:
                 continue
             A__					=       model_class(__UpperCAmelCase)
             model.to(__UpperCAmelCase)
             model.gradient_checkpointing_enable()
             model.train()
             A__					=       self._prepare_for_class(__UpperCAmelCase			,    __UpperCAmelCase			,    return_labels=__UpperCAmelCase)
             A__					=       model(**__UpperCAmelCase).loss
             loss.backward()




     def 		snake_case_		(					self):
         A__					=       self.model_tester.prepare_config_and_inputs_for_common()

         A__					=       _config_zero_init(__UpperCAmelCase)
         for model_class in self.all_model_classes:
             A__					=       model_class(config=__UpperCAmelCase)
             # Skip the check for the backbone
             A__					=       []
             for name, module in model.named_modules():
                 if module.__class__.__name__ == "DPTViTHybridEmbeddings":
                     A__					=       [F"{name}.{key}" for key in module.state_dict().keys()]
                     break

             for name, param in model.named_parameters():
                 if param.requires_grad:
                     if name in backbone_params:
                         continue
                     self.assertIn(
                         ((param.data.mean() * 1e9).round() / 1e9).item()			,    [0.0, 1.0]			,    msg=F"Parameter {name} of model {model_class} seems not properly initialized"			,    )




     @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''')
     def 		snake_case_		(					self):
         pass




     @slow
     def 		snake_case_		(					self):
         for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
             A__					=       DPTModel.from_pretrained(__UpperCAmelCase)
             self.assertIsNotNone(__UpperCAmelCase)




     def 		snake_case_		(					self):
         # We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
         A__					=       self.model_tester.prepare_config_and_inputs_for_common()
         A__					=       '''add'''
         with self.assertRaises(__UpperCAmelCase):
             A__					=       DPTForDepthEstimation(__UpperCAmelCase)







def 		lowerCAmelCase__     (       )->	int:
    A__					=       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
    return image



@require_torch
@require_vision
@slow
class      _UpperCAmelCase      (							unittest.TestCase  ):




     def 		snake_case_		(					self):
         A__					=       DPTImageProcessor.from_pretrained('''Intel/dpt-hybrid-midas''')
         A__					=       DPTForDepthEstimation.from_pretrained('''Intel/dpt-hybrid-midas''').to(__UpperCAmelCase)

         A__					=       prepare_img()
         A__					=       image_processor(images=__UpperCAmelCase			,    return_tensors='''pt''').to(__UpperCAmelCase)

         # forward pass
         with torch.no_grad():
             A__					=       model(**__UpperCAmelCase)
             A__					=       outputs.predicted_depth

         # verify the predicted depth
         A__					=       torch.Size((1, 3_8_4, 3_8_4))
         self.assertEqual(predicted_depth.shape			,    __UpperCAmelCase)

         A__					=       torch.tensor(
             [[[5.6_4_3_7, 5.6_1_4_6, 5.6_5_1_1], [5.4_3_7_1, 5.5_6_4_9, 5.5_9_5_8], [5.5_2_1_5, 5.5_1_8_4, 5.5_2_9_3]]]).to(__UpperCAmelCase)

         self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 1_0_0			,    __UpperCAmelCase			,    atol=1e-4))



from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
		import PIL


lowercase			:				Tuple   =     logging.get_logger(__name__)







class 					__lowercase  (						_SCREAMING_SNAKE_CASE				):





							"""simple docstring"""

							UpperCAmelCase_						:		Optional[Any]								=	['''pixel_values''']




							def __init__(					self    ,						__UpperCAmelCase = True    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = PIL.Image.BICUBIC    ,						__UpperCAmelCase = True    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = 1 / 2_55    ,						__UpperCAmelCase = True    ,						__UpperCAmelCase = True    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						**__UpperCAmelCase    ,						)		->       None:
														super().__init__(**__UpperCAmelCase						)
														A      :					Any      		=					size if size is not None else {'''height''': 2_56, '''width''': 2_56}
														A      :					Any      		=					get_size_dict(__UpperCAmelCase						)
														A      :					List[Any]      		=					crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
														A      :					List[Any]      		=					get_size_dict(__UpperCAmelCase    ,						param_name='''crop_size'''						)

														A      :					Dict      		=					do_resize
														A      :					Tuple      		=					size
														A      :					Union[str, Any]      		=					resample
														A      :					Dict      		=					do_center_crop
														A      :					int      		=					crop_size
														A      :					Union[str, Any]      		=					do_rescale
														A      :					str      		=					rescale_factor
														A      :					Optional[Any]      		=					do_normalize
														A      :					Dict      		=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
														A      :					Dict      		=					image_std if image_std is not None else IMAGENET_STANDARD_STD




							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase = PIL.Image.BICUBIC    ,						__UpperCAmelCase = None    ,						**__UpperCAmelCase    ,						)		->       np.ndarray:
														A      :					int      		=					get_size_dict(__UpperCAmelCase						)
														if "height" not in size or "width" not in size:
																					raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'						)
														return resize(
														    __UpperCAmelCase    ,						size=(size['''height'''], size['''width'''])    ,						resample=__UpperCAmelCase    ,						data_format=__UpperCAmelCase    ,						**__UpperCAmelCase						)




							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase = None    ,						**__UpperCAmelCase    ,						)		->       np.ndarray:
														A      :					Optional[Any]      		=					get_size_dict(__UpperCAmelCase						)
														if "height" not in size or "width" not in size:
																					raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'						)
														return center_crop(__UpperCAmelCase    ,						size=(size['''height'''], size['''width'''])    ,						data_format=__UpperCAmelCase    ,						**__UpperCAmelCase						)




							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase = None    ,						**__UpperCAmelCase    ,						)		->       List[str]:
														return rescale(__UpperCAmelCase    ,						scale=__UpperCAmelCase    ,						data_format=__UpperCAmelCase    ,						**__UpperCAmelCase						)




							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase = None    ,						**__UpperCAmelCase    ,						)		->       np.ndarray:
														return normalize(__UpperCAmelCase    ,						mean=__UpperCAmelCase    ,						std=__UpperCAmelCase    ,						data_format=__UpperCAmelCase    ,						**__UpperCAmelCase						)






							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase=None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = ChannelDimension.FIRST    ,						**__UpperCAmelCase    ,						)		->       PIL.Image.Image:
														A      :					Optional[int]      		=					do_resize if do_resize is not None else self.do_resize
														A      :					Optional[Any]      		=					resample if resample is not None else self.resample
														A      :					Tuple      		=					do_center_crop if do_center_crop is not None else self.do_center_crop
														A      :					Optional[Any]      		=					do_rescale if do_rescale is not None else self.do_rescale
														A      :					str      		=					rescale_factor if rescale_factor is not None else self.rescale_factor
														A      :					Tuple      		=					do_normalize if do_normalize is not None else self.do_normalize
														A      :					Optional[int]      		=					image_mean if image_mean is not None else self.image_mean
														A      :					Dict      		=					image_std if image_std is not None else self.image_std

														A      :					List[Any]      		=					size if size is not None else self.size
														A      :					List[Any]      		=					get_size_dict(__UpperCAmelCase						)
														A      :					Dict      		=					crop_size if crop_size is not None else self.crop_size
														A      :					Tuple      		=					get_size_dict(__UpperCAmelCase    ,						param_name='''crop_size'''						)

														A      :					Any      		=					make_list_of_images(__UpperCAmelCase						)

														if not valid_images(__UpperCAmelCase						):
																					raise ValueError(
																					    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
																					    '''torch.Tensor, tf.Tensor or jax.ndarray.'''						)

														if do_resize and size is None or resample is None:
																					raise ValueError('''Size and resample must be specified if do_resize is True.'''						)

														if do_center_crop and crop_size is None:
																					raise ValueError('''Crop size must be specified if do_center_crop is True.'''						)

														if do_rescale and rescale_factor is None:
																					raise ValueError('''Rescale factor must be specified if do_rescale is True.'''						)

														if do_normalize and (image_mean is None or image_std is None):
																					raise ValueError('''Image mean and std must be specified if do_normalize is True.'''						)

														# All transformations expect numpy arrays.
														A      :					List[Any]      		=					[to_numpy_array(__UpperCAmelCase						) for image in images]

														if do_resize:
																					A      :					List[str]      		=					[self.resize(image=__UpperCAmelCase    ,						size=__UpperCAmelCase    ,						resample=__UpperCAmelCase						) for image in images]

														if do_center_crop:
																					A      :					Dict      		=					[self.center_crop(image=__UpperCAmelCase    ,						size=__UpperCAmelCase						) for image in images]

														if do_rescale:
																					A      :					Union[str, Any]      		=					[self.rescale(image=__UpperCAmelCase    ,						scale=__UpperCAmelCase						) for image in images]

														if do_normalize:
																					A      :					Tuple      		=					[self.normalize(image=__UpperCAmelCase    ,						mean=__UpperCAmelCase    ,						std=__UpperCAmelCase						) for image in images]

														A      :					Optional[Any]      		=					[to_channel_dimension_format(__UpperCAmelCase    ,						__UpperCAmelCase						) for image in images]

														A      :					List[Any]      		=					{'''pixel_values''': images}
														return BatchFeature(data=__UpperCAmelCase    ,						tensor_type=__UpperCAmelCase						)






